Triple

T9349303
Position Surface form Disambiguated ID Type / Status
Subject Limburg an der Lahn E224973 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object LM
LM is the vehicle registration code for the German town of Limburg an der Lahn in the state of Hesse.
 E792540 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: LM | Statement: [Limburg an der Lahn, vehicleRegistrationCode, LM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LM
Context triple: [Limburg an der Lahn, vehicleRegistrationCode, LM]
  • A. LM
    LM is the IATA airline designator assigned to Loganair, a regional airline based in Scotland.
  • B. LM
    LM is the Apollo Lunar Module, the spacecraft used by NASA during the Apollo program to land astronauts on the Moon and return them to lunar orbit.
  • C. LM
    LM is the vehicle registration code used for cars registered in the town of Liptovský Mikuláš in Slovakia.
  • D. Lm
    Lm is the currency symbol that was used to denote the Maltese lira, Malta’s former national currency before adoption of the euro.
  • E. LEM
    LEM is the original abbreviation for the Apollo Lunar Module, the spacecraft used by NASA astronauts to land on and ascend from the Moon during the Apollo missions.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: LM
Triple: [Limburg an der Lahn, vehicleRegistrationCode, LM]
Generated description
LM is the vehicle registration code for the German town of Limburg an der Lahn in the state of Hesse.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LM
Target entity description: LM is the vehicle registration code for the German town of Limburg an der Lahn in the state of Hesse.
  • A. LM
    LM is the IATA airline designator assigned to Loganair, a regional airline based in Scotland.
  • B. LM
    LM is the Apollo Lunar Module, the spacecraft used by NASA during the Apollo program to land astronauts on the Moon and return them to lunar orbit.
  • C. LM
    LM is the vehicle registration code used for cars registered in the town of Liptovský Mikuláš in Slovakia.
  • D. Lm
    Lm is the currency symbol that was used to denote the Maltese lira, Malta’s former national currency before adoption of the euro.
  • E. LEM
    LEM is the original abbreviation for the Apollo Lunar Module, the spacecraft used by NASA astronauts to land on and ascend from the Moon during the Apollo missions.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca842abfd48190949d71c3b86eeba8 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd4f1198b88190adc0b01f7c1be36e completed April 1, 2026, 5 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0e43c31008190b542a9c5aa33f30e completed April 4, 2026, 10:13 a.m.
NEDg Description generation batch_69d0e587586c8190a73ce417f0b5adec completed April 4, 2026, 10:18 a.m.
NED2 Entity disambiguation (via description) batch_69d0e5eb2ce88190973acc2cc8ce254f completed April 4, 2026, 10:20 a.m.
Created at: March 30, 2026, 7:41 p.m.